An innovative fire prediction system designed to safeguard communities from the threat of wildfires. Utilizing advanced machine learning algorithms, It analyzes environmental factors such as temperature, humidity, wind speed, and more to provide accurate predictions of fire outbreaks.
The linear regression model and random forest regressor predict the continuous value of FWI, providing a crucial assessment of fire weather conditions. Simultaneously, the categorical classifiers enhance the overall model by determining the likelihood of fire occurrence. Among the classifiers, Random Forest demonstrates exceptional accuracy, achieving a great score for binary fire occurrence classification.